fonctions.py

from io import BytesIO
import requests
import re
import pandas as pd
from openai import OpenAI
import os
from langchain_core.messages import convert_to_messages


api_open_ai_agent_key=os.environ["OPENAI_API_KEY"]
client = OpenAI(api_key=api_open_ai_agent_key)


def pretty_print_message(message, indent=False):
    pretty_message = message.pretty_repr(html=True)
    if not indent:
        print(pretty_message)
        return

    indented = "\n".join("\t" + c for c in pretty_message.split("\n"))
    print(indented)


def pretty_print_messages(update, last_message=False):
    is_subgraph = False
    if isinstance(update, tuple):
        ns, update = update
        # skip parent graph updates in the printouts
        if len(ns) == 0:
            return

        graph_id = ns[-1].split(":")[0]
        print(f"Update from subgraph {graph_id}:")
        print("\n")
        is_subgraph = True

    for node_name, node_update in update.items():
        update_label = f"Update from node {node_name}:"
        if is_subgraph:
            update_label = "\t" + update_label

        print(update_label)
        print("\n")

        messages = convert_to_messages(node_update["messages"])
        if last_message:
            messages = messages[-1:]

        for m in messages:
            pretty_print_message(m, indent=is_subgraph)
        print("\n")
def clean_response(response):
    match = re.search(r'FINAL ANSWER:\s*(.+)', response['supervisor']['messages'][-1].content)
    answer = match.group(1).strip() if match else None
    if answer is None :
        answer = "pas de réponse"
    return answer
def response_from_agent(supervisor, question):

    for chunk in supervisor.stream(
            {"messages": [{"role": "user", "content": question}]}
    ):

        response = chunk
        pretty_print_messages(chunk)

    response = clean_response(response)
    return response


def load_data(question):
    task_id = question.get('task_id')
    file_name = question.get('file_name')

    if file_name == "":
        return 'There is no attached file'

    files_response = requests.get(f"https://agents-course-unit4-scoring.hf.space/files/{task_id}")
    if files_response.status_code == 404:
        return 'Le lien ne fonctionne pas'
    if file_name.endswith('.xlsx'):
        excel_data = BytesIO(files_response.content)
        df = pd.read_excel(excel_data)
        data_dict = df.to_dict(orient="list")
        return data_dict

    elif file_name.endswith('.png'):
        response = client.responses.create(
            model="gpt-4.1-mini",
            input=[{
                "role": "user",
                "content": [
                    {"type": "input_text", "text": "what's in this image? Please give as much details as possible"},
                    {
                        "type": "input_image",
                        "image_url": f"https://agents-course-unit4-scoring.hf.space/files/{task_id}",
                    },
                ],
            }],
        )

        return response.output_text

    elif file_name.endswith('.mp3'):
        audio_bytes = BytesIO(files_response.content)
        audio_bytes.name = "audio.mp3"

        transcription = client.audio.transcriptions.create(
            model="whisper-1",  # ou "whisper-1", mais "gpt-4o" est aussi correct
            file=audio_bytes
        )

        return transcription.text

    else :
        return 'there is no attached file'